Pipeline flow control optimization software methods

ABSTRACT

Pipeline flow optimization systems, software, and methods that emulate transient optimization are disclosed. A pipeline flow optimization system includes an upset condition handler that is adapted to provide a multivariable controller with pipeline flow adjustments to handle one or more upset conditions. The optimization system may be used with fluid pipeline systems, including gas pipeline systems and liquid pipeline systems. Systems, software, and methods according to embodiments of the invention may further include linepack transition handling capabilities and administrative tools.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 60/584,676, filed on Jul. 2, 2004, which is herebyincorporated by reference for all purposes as if fully set forth herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to software and methods for fluid pipelineoptimization and control, and more specifically, to optimization andcontrol software and methods that can optimize for transient conditionswithin the fluid pipeline.

2. Description of Related Art

Fluid pipeline systems, such as gas transmission pipelines, operate atcertain pressures and with certain flow rates in order to deliver fluid,for example, natural gas, to its destination. In the case of a gaspipeline, compressors along the pipeline maintain the pressuresnecessary to move the gas. In the case of a liquid pipeline, pumps movethe liquid along the pipeline.

FIG. 1 is a diagram showing a typical gas pipeline system. Generally,referring to FIG. 1, a typical gas pipeline system may include multiplerelay compressor stations with multiple points of supply and deliveries.For example, FIG. 1 shows a pipeline system having 6 compressorstations. Each individual station is controlled by control logic that,among other things, operates compressors and prevents a station fromexceeding its maximum allowable operating pressures. The pipelineoperator, through a supervisory control and data acquisition (SCADA)system, gives each individual compressor station's control logic its owntarget setpoints for suction pressure, discharge pressure and flow. Thestation's control logic seeks to maintain these target setpoints bystarting and stopping compressors, as necessary. Additionally, as notedabove, the control logic protects the station from exceeding its maximumallowable operating pressures and maintains it within safe operatingparameters. However, the station control logic controls an individualstation. Hence, it functions independent of the other stations in thesystem.

FIG. 2 is a diagram showing a pipeline flow control system that may beutilized to control the pipeline system of FIG. 1. Generally, referringto FIG. 2, a pipeline flow control system may include steady-stateOptimization Software that calculates optimal target setpoints for thepipeline operator to manually send to the individual stations' controllogic. Further, as FIG. 2 shows, the system may also include amultivariable control system (“Controller Software”) operating inconjunction with the optimization software. The multivariable controllersoftware may include a set of controllers that attempts to drive a setof station discharge pressures to equal a set of discharge pressuretargets calculated by the optimization software, and there may be onecontroller for each compressor station that is operating undermultivariable control. Each controller attempts to manipulate thesuction pressure of the compressor station it controls in order to drivethe discharge pressure of the next upstream station toward its optimaldischarge pressure target.

However, fluid pipelines are dynamic systems, and the control systemsdescribed with reference to FIG. 1 and FIG. 2 may not anticipate or takeinto account certain transient (i.e. non-steady state) conditions.Additionally, because each controller in the system of FIG. 2 typicallycontrols its station in light of operating conditions at that stationand the next upstream station only, the controller software iscontrolling individual stations on the pipeline, which are allinterconnected, based on data gleaned from only a portion of thestations in the system. Hence, the controller software may noteffectively control all stations on the pipeline system in light oftransient conditions affecting the entire pipeline system or otherportions of the pipeline system beyond the control of an individualcontroller. Accordingly, the controller software may, among otherthings, increase the cost of transporting fluid through the pipeline.

Further, the system shown in FIG. 2 utilizes a single set of tuneparameters to handle all transient conditions but that are aimed at onlycontrolling the setpoints of the immediate upstream compressor station.However, certain transient conditions may occur in the pipeline systemthat can not be efficiently or effectively managed by only controllingthe setpoints of the immediate upstream compressor station. In suchtransient conditions, the controller software's single set of tuneparameters does not provide the capability to adjust station operatingparameters to effectively or timely handle the transient conditions.Hence, in this case, the stations' control logic over-rides themultivariable controller software and controls corresponding individualcompressor station targets to near maximum operating pressures.Generally, this leads to an imbalance in the system, which usuallyresults in not being able to take the full contracted supplies into thetransmission system.

Additionally, the control system of FIG. 2 is not able to identify ortransition to a new optimal solution when conditions change (e.g.supplies or deliveries of the transmission system) that require a changeto the optimal compressor configuration. A particular problemencountered was when to start additional compressors when required foran optimal fuel-efficient system. Starting the additional compressorstoo early wastes fuel. On the other hand, starting the additionalcompressors too late may cause a transient condition that is beyond thecapability of the controller software to control, resulting in stationcontrol logic overriding the controller software.

SUMMARY OF THE INVENTION

The present invention provides a pipeline flow optimization system thatemulates transient optimization.

Additional features of the invention will be set forth in thedescription which follows, and in part will be apparent from thedescription, or may be learned by practice of the invention.

The present invention discloses a pipeline flow optimization system,comprising an upset condition handler adapted to provide a multivariablecontroller with pipeline flow adjustments to handle one or more upsetconditions.

The present invention also discloses a pipeline flow optimization systemincluding an upset condition handler, a multivariable controller forcontrolling components of a pipeline system, optimization software forcalculating steady-state parameters of the pipeline system and providingthe steady-state parameters to the upset condition handler, and asupervisory control and data acquisition system for acquiring pipelinesystem data. The upset condition handler receives the pipeline systemdata from the supervisory control and data acquisition system andprovides the multivariable controller with pipeline flow adjustments tohandle one or more upset conditions.

The present invention also disclose a pipeline flow control systemincluding an optimizer for calculating steady-state parameters of apipeline system, a controller for controlling pipeline system componentsthat cause pipeline flow, a data acquirer for acquiring pipeline systemcomponent data and pipeline flow data, and a control logic unit. Thecontrol logic unit adjusts the steady-state parameters of the pipelinesystem in response to an upset condition by analyzing the pipelinesystem component data and the pipeline flow data and applying anadjustment factor to the controller.

The present invention also discloses a method of handling transientconditions in a pipeline flow optimization system. In the method, one ormore adjustment factors are calculated. Each of the adjustment factorsis indicative of a degree to which a correction is needed for an upsetcondition. The adjustment factors are stored, one or more of theadjustment factors are chosen and applied to the pipeline flowoptimization system.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description serve to explain the principles of theinvention.

FIG. 1 is a diagram showing a typical gas pipeline system.

FIG. 2 is a diagram showing a pipeline flow control system that may beutilized to control the pipeline system of FIG. 1.

FIG. 3 is a diagram showing an automated pipeline optimization systemaccording to an embodiment of the invention.

FIG. 4, FIG. 5, FIG. 15, FIG. 16, and FIG. 17 are schematic flowdiagrams illustrating a pipeline flow management system in accordancewith embodiments of the invention.

FIG. 6 and FIG. 7 are schematic flow diagrams illustrating the generalprocess of computing and applying adjustment factors for a number ofupset conditions.

FIG. 8 and FIG. 12 are schematic flow diagrams illustrating thecomputation of adjustment factors for a number of upset conditions.

FIG. 9, FIG. 10, FIG. 11, and FIG. 13 are schematic flow diagramsillustrating expressions used to compute adjustment factors for variousupset conditions.

FIG. 14 is a schematic flow diagram illustrating the application of thecomputed adjustment factors to the pipeline flow control optimizationsystem.

FIG. 18 and FIG. 19 show steps for saving a revised current nominationand a future nomination, respectively, and FIG. 20, FIG. 21, FIG. 22,FIG. 23, FIG. 24, and FIG. 25 are flow charts illustrating a transitionprocess according to an embodiment of the invention.

FIG. 26 shows general calibration processes that may be completed eachday, and FIG. 27, FIG. 28, FIG. 29, FIG. 30, FIG. 31, and FIG. 32 areschematic flow diagrams illustrating tasks involved in calibrating thesystem according to an embodiment of the invention.

FIG. 33, FIG. 34, FIG. 35, and FIG. 36 are flow charts illustratingsteps of a method for accurately measuring the fuel savings of thesystem according to an embodiment of the invention.

FIG. 37 shows fuel attainment variances that may be taken into accountwhen measuring the fuel savings of the system according to an embodimentof the invention.

DETAILED DESCRIPTION

As was described above, aspects of the invention relate to systems andmethods for fluid pipeline flow optimization and, in particular, systemsand methods for fluid pipeline flow optimization that are capable ofhandling transient or upset conditions. In their broadest embodiments,software and methods according to the invention ensure that the demandfor a fluid flowing in a pipeline is met. That demand is typicallysubject to certain requirements, for example, that particular volumes ofthe fluid be delivered at pressures between specified minimum andmaximum delivery pressures. Additionally, systems and methods accordingto the invention ensure that fluid pipeline transmission facilities areoperating within the engineering design parameters for which they weredesigned. In general, those two requirements are met by regularmonitoring and by controlling fluid pipeline parameters to keep themwithin acceptable tolerances (sometimes referred to as “deadbands”).Systems and methods according to embodiments of the invention may alsoprovide human controllers with feedback as to the pipeline's presentfuel efficiency, and may provide engineers with calibration toolsnecessary to tune software, systems, and methods according to theinvention.

Software according to embodiments of the invention provides one or moreof the following three modules or capabilities: upset conditionalgorithms and upset condition handling, linepack transition handling,and administrative tools. Upset condition algorithms recognize whenconditions vary from the static optimization and change compressorsetpoints along the gas pipeline to account for the upset whilecontinuing to optimize fuel efficiency within the given compressor unitconfiguration. Linepack transition handling algorithms calculate thetime required to attain optimal pressure targets when a future optimalrun dictates a change in compressor unit configuration. Administrativetools comprise measurement methods used to determine fuel efficiencyperformance, and allow accurate cost savings reports to be generated.Administrative tools may also include calibration tools used to tune themodels created by the software.

Although most aspects and embodiments of the invention are equallyapplicable to any type of fluid pipeline system, including gas pipelinesand liquid pipelines, certain embodiments of the invention will bedescribed with respect to gas transmission pipeline networks.

FIG. 3 shows an automated pipeline optimization (APO) system accordingto an embodiment of the invention. Referring to FIG. 3, systems andmethods according to gas pipeline-based embodiments of the inventiontypically combine a multivariable control system (“Controller software”)with optimization simulation programming (“Optimization Software”)through a supervisory control and data acquisition (SCADA) system toreduce fuel consumption at compressor stations along the gas pipelinenetwork. The APO system utilizes an upset condition handler and alinepack transition handler (“Black Box”) to optimize pipelineoperations by accounting for and handling-all upset conditions andlinepack transitions. The upset condition handler handles transientconditions, including those that previously would have caused thestations' control logic to override the controller software. The APOsystem detects transient conditions occurring throughout the pipelinesystem, evaluates and selects a transient condition to address, and thenchooses an optimal solution, from among multiple potential solutions, toaddress the selected condition.

Accordingly, utilizing the Administrative Tools, the Upset ConditionHandler, and the Linepack Transition Handler in the pipeline flowoptimization system of FIG. 3 provides a system that may maintainoptimal steady-state configurations and properly adjust optimalsteady-state pressure targets through upset conditions; that mayidentify through existing field compressor logic, or supply or deliverychange of the predictive models, when a new compressor configuration maybe required; that may quickly transition to the new optimal solution;that may verify results through a verification process of an AutomatedCalibration Tool; and that may quantify the results of the pipelineoptimization process through a Fuel Attainment process.

In embodiments of the invention, the methods according to the inventionmay be incorporated into the multivariable control system, or they maybe implemented separately, as shown in FIG. 3, in which case they wouldbe used to generate inputs into the multivariable control system.Although any multivariable control system may be used, GE-CCI's MVC-4.0®multivariable control system is one suitable system. Further, althoughany optimization software may be used, Advantica's Stoner Gas Solver forEMM (Energy Minimization Module) is one suitable package. Whether aloneor as part of a multivariable control system, systems, methods andsoftware according to embodiments of the invention may be programmed andimplemented in any one of a number of programming languages, includingC, C++, C#, Java, Visual Basic, and any other programming language ableto implement the tasks of the methods.

One embodiment of the invention was implemented for testing using asection of gas transmission line including approximately 220 miles ofgas transmission piping with six compressor stations spaced 40-45 milesapart along the gas transmission line, and certain portions of thedescription that follows will refer to specific locations along thattestbed gas transmission line. However, it should be understood thatsystems, methods, and software according to embodiments of the inventionmay be implemented in fluid pipeline systems of any size or complexity.

FIG. 4, FIG. 15, FIG. 16 and FIG. 17 are schematic flow diagrams showingthe overall control logic used to select the mode of the system. Eachtime the system is turned on, the total available horsepower is summed,and other power-on and initialization tasks may be performed. (Severalexemplary tasks are shown in FIG. 4). Once the initialization tasks areperformed as per FIG. 4 and FIG. 5, the control logic (i.e. the upsetcondition handler and the controller software) monitors the operatingmode of the system. During times when the system is operating normally,the system is kept in “normal” mode. In “normal” mode, the system canhandle transient conditions and changes within certain tolerances.

If a severe problem should occur in the pipeline, such as the unexpectedshutdown of a compressor that is needed to maintain system integrity,the control logic changes the mode of the system for that particularcompressor to “event.” In “event mode,” the operator is notified and thecontrol logic that would normally allow compressors to operate at ornear 100% of their rated power without starting new compressors isdeactivated so as to facilitate starting replacement compressors. Inaddition, appropriate pressure set points upstream of the station in“event” mode are reduced to “event” mode settings to minimize theprobability that the upstream station will shut down because ofover-pressure alarm conditions. The appropriate target pressuresdownstream from the station in “event” mode are reduced by thealgorithms described above and below. The operator may also takeadditional actions to resolve the problem. When “event mode” isterminated, the system resets the settings to conform to “normal mode”settings. As compared with standard multivariable control systems,“event” mode in systems according to the present invention is typicallyinvoked only selectively and only to the extent needed to maintainsystem integrity.

In both “normal” and “event” modes, the system uses a factor- orparameter-based method for dealing with upset or transient conditions.This method is generally illustrated in the schematic flow diagram ofFIG. 6. The actual upset or transient conditions that the system detectsand handles may vary from pipeline to pipeline, and are generallydefined depending on the type of pipeline and the needs of the users.However, the parameter-based method will be described with respect tocertain standard upset conditions for a gas pipeline.

As shown in FIG. 6, in general, the system initializes an array or otherdata structure. In other words, the system clears out old data andinputs current data. Prior to initializing the array or data structure,the system determines whether to adjust a flow target, as shown in FIG.6 and FIG. 7. Each data location within the array or other datastructure contains a value for an adjustment factor indicative of thedegree to which a correction is needed for a particular upset condition.The system engages in an iterative process of calculating the values ofeach of the adjustment factors and storing them in the array or otherdata structure. Once values for each adjustment factor have beencalculated, the system selects and applies the most necessary of theadjustment factors. Typically, the most necessary of the adjustmentfactors would be the adjustment factors with the largest values in thearray or other data structure, although this need not be the case.

For example, in FIG. 6, the array contains space for the values of tenadjustment factors, indexed 1-10, although as many adjustment factors asdesired may be monitored by systems, methods, and software according tothe invention. It should also be understood that the upset conditionsand corresponding adjustment factors may include both adjustment factorsfor which the system can take automatic action to correct and adjustmentfactors for which the only action is to notify the human operator of thecondition. This will be described below in more detail.

FIG. 8 and FIG. 12 are schematic flow diagrams illustrating theiterative calculation of each of the adjustment factors for therespective upset condition. As shown in FIG. 8, the first upsetcondition (j=1) is the condition in which the flow at the intake(“Station #1” in the figure) is less than the target flow rate. Thesecond upset condition (j=2) is the condition in which an upstreamcompressor station is in a high-flow transient condition. The thirdupset condition (j=3) is the condition in which one or more of thecompressor station discharge pressures is close to or at the maximumoperating pressure (“MOP” in the figure). The fourth upset condition(j=4) is the upset condition in which the throttle or speed driver ofone or more compressors is near 100%. The fifth upset condition (j=5) isthe upset condition in which the throttle or power of one or morecompressors is near 0%. The sixth upset condition (j=6) is the upsetcondition in which the cooler temperature is close to maximum. Theseventh upset condition (j=7) is the upset condition in which an engineneeds to start in a station that is already running. Referring to FIG.12, the eighth upset condition (j=8) is the upset condition in which adischarge pressure at a particular location (“Station #5” in the figure)is close to a delivery pressure required by a customer (“Customer #1” inthe figure). The ninth upset condition (j=9) is the upset condition inwhich the supply pressure at a particular point in the pipeline(“Supplier #3” in the figure) is close to the pressure in the pipeline.The tenth upset condition (j=10) is the upset condition in which thedelivery pressure at a particular point in the pipeline (“Customer #2delivery pressure” in the figure) is close to the pressure in thepipeline. The eighth, ninth, and tenth upset conditions are conditionsthat the multivariable control system may not be able to directlyaddress, so action on those three upset conditions is limited tonotifying the human operator. However, as was noted above, the array ofadjustment factors for upset conditions may include any number offactors and may include both factors for upset conditions that can behandled automatically and factors that require operator notification inany combination. In some embodiments, only factors that can be handledautomatically may be included in the array or other data structure. Asshown in FIG. 6, FIG. 8 and FIG. 12, once adjustment factors arecalculated for each of the ten upset conditions defined in thisembodiment of the invention, the appropriate adjustment factors arechosen and applied.

FIG. 9 is a schematic flow diagram illustrating the calculation of thefirst through third adjustment factors (j=1, j=2, and j=3). As wasdescribed above, the first adjustment factor relates to the upsetcondition in which the flow at the intake point in the pipeline is lessthan the target flow. In this upset condition, it is assumed that thegas supplier will eventually increase the flow into the pipeline so asto meet their contractual obligations. Therefore, in the case of lowflow at the intake, systems according to the invention lower thedischarge pressure target at the intake. This creates capacity in thesystem downstream to accommodate the gas that will be pumped into thesystem later. It also reduces the stress on the compressors as the gasis added at increased flow rates later. The expression used to calculatethe first adjustment factor is shown in FIG. 9. Descriptions of thevariables used in FIG. 9 are given below in TABLES 1 and 2.

As was described above, the second adjustment factor relates to theupset condition in which the discharge pressure is greater than thetarget discharge pressure. When the discharge pressure is greater thanthe target discharge pressure, systems according to embodiments of theinvention lower the target discharge pressure at downstream compressorstations. This causes downstream compressors to speed up faster andreduces excess linepack. Essentially, it improves the response time ofthe system. The expression used to calculate the adjustment factor isshown in FIG. 9.

The third adjustment factor, the calculation of which is also shown inFIG. 9, relates to the upset condition in which a discharge pressure atone of the compressor stations is close to the maximum operatingpressure in the pipeline. As those of skill in the art will appreciate,having the discharge pressure at a compressor station at a pressureclose to the maximum operating pressure is generally an undesirablecondition. Therefore, in this condition, the system lowers the targetdischarge pressure at downstream compressor stations so that thedownstream compressors will “pull” the pressure away. The expressionused to calculate the adjustment factor is shown in FIG. 9.

FIG. 10 is a schematic flow diagram illustrating the calculation of thefourth, fifth, and sixth adjustment factors (j=4, j=5, and j=6). As wasdescribed above, the fourth adjustment factor relates to the upsetcondition in which the throttle or speed driver of one or more of thecompressors is close to 100%. Therefore, in this condition, the systemlowers the target discharge pressure downstream, which reduces the speeddriver or throttle of the downstream compressors and allows the supplypressure to control the pressure in the pipeline. The expression used tocalculate the adjustment factor is shown in FIG. 10. Descriptions of thevariables used in FIG. 10 are given below in TABLES 1 and 2.

The fifth adjustment factor relates to the upset condition in which thethrottle or speed driver of one or more of the compressors is close to0%. When the speed driver is close to 0%, the discharge pressure isconstrained and will not be able to decrease. In this condition, thesystem lowers the discharge pressure target, which creates moreavailable capacity downstream in the pipeline. Then if a compressorstation down stream should crash, there will be sufficient time torecover before the station near 0% speed driver also crashes due to overpressure. The expression used to calculate the adjustment factor isshown in FIG. 10.

The sixth adjustment factor relates to the upset condition in which thecooler temperature is close to maximum. When the cooler temperature isclose to maximum, the suction pressure is constrained and will not beable to decrease. In this condition, the system lowers the dischargepressure target, which reduces the compression ratio and lowers thedischarge temperature unconstraining the suction pressure. Theexpression used to calculate the adjustment factor is shown in FIG. 10.

FIG. 11 is a schematic flow diagram illustrating the calculation of theseventh adjustment factor (j=7). As was described above, the seventhadjustment factor relates to the upset condition in which an engineneeds to start in a station that is already running. When an engineneeds to start in a station that is already running, the stationcontroller will not allow the engine to start if the discharge pressureis greater than the limit for starting engines. In this condition, ifthe discharge pressure is greater than the limit for starting an engine,the system reduces the discharge pressure in time to start the engine.The expression used to calculate the adjustment factor is shown in FIG.11.

FIG. 13 is a schematic flow diagram illustrating the calculation of theeighth, ninth, and tenth (j=8, j=9, and j=10). The eighth adjustmentfactor relates to an upset condition for which automatic handling is notavailable, although in some embodiments, the system might handle theupset condition automatically, at least to some extent. In the upsetcondition to which the eighth adjustment factor relates, the pressure ata particular point in the pipeline (“Station #5”, as identified in FIG.13) is close to the limiting pressure required by a customer (“Customer#1”, as identified in the figure). (More particularly, the algorithmqueries whether a particular transmission line is flowing to thesupplier and, if so, to what degree a valve is open.) If the conditionis true, a warning such as, “Customer #1 pressure close to Station #5'sdischarge header #2 pressure. Either change Station #5 loopconfiguration or raise downstream suction pressure,” may be provided.The expressions used to calculate the eighth adjustment factor are shownin FIG. 13. The ninth and tenth adjustment factors also relate topressure and flow upset conditions within the pipeline that might makeit difficult for the pipeline to supply a particular flow rate of gas ata particular pressure so as to meet contractual obligations. In eachcase, the operator is provided with a warning similar to that describedabove, and appropriate action is taken. The expressions used tocalculate the ninth and tenth adjustment factors are shown in FIG. 13.

As those of skill in the art will note, the first seven adjustmentfactors deal with transient conditions in the pipeline that can becontrolled by the pipeline control system, whereas the eighth, ninth andtenth adjustment factors deal with upset conditions that are outside thescope of control of the pipeline control system. Although thisdistinction applies in the present example and may not apply in allembodiments of the invention, the capacity of software, systems, andmethods according to the invention to indicate possible problems in thepipeline that are beyond the scope of the multivariable control systemlogic to handle may be a beneficial feature. Additionally, as was notedabove, the multivariable control system may also be programmed to handlethe kinds of upset conditions represented by the eighth, ninth and tenthadjustment factors. Furthermore, the additional adjustment factors maybe factors required for or desirable in specific pipelines or inspecific configurations of software, systems and methods according tothe invention.

TABLE 1 Variables: Description: Q Flow Rate value into Station #1(MMCFD) FT Flow Target now into Station #1 (MMCFD) FFT Future FlowTarget into Station #1 (MMCFD) c # of Compressor Station: Station #1 =0, Station #2 = 1, Station #3 = 2, Station #4 = 3, Station #5 = 4,Station #6 = 5 s # of Downstream Compressor Station whose TargetDischarge Pressure is controlled by Control Loop t # of UpstreamCompressor Stations whose upstream of a station in Event Mode.Event_Offset Discharge Pressure Target Offset for stations upstream of astation in Event Mode. TN Current Time (Microsoft Excel Datetime formatcurrent time value adjusted to MVC time) TS Time Start Time future flowtarget becomes effective (Datetime format) TB Time Flow Target iscomputed to Begin increasing from prior value to future value (Datetimeformat) TE Time Flow Target is computed to reach future value (Datetimeformat) LT Lead Time (Deed Band) Value Controlled Discharge Manipulatedi (Control Loop) t Pressure Target s Suction Pressure 0 0 Station #1 1Station #2 1 0 Station #1 2 Station #3 2 1 Station #2 2 Station #3 3 1Station #2 3 Station #4 4 2 Station #3 3 Station #4 5 2 Station #3 4Station #6 6 3 Station #4 4 Station #5 7 3 Station #4 5 Station #6 8 4Station #5 5 Station #6 Adjustment j Value Factor: Description 0 AdjustFlow Target for New Nomination 1 Compensate for Station #1 flow lessthan target 2 Compensate for upstream station in high flow transientcondition 3 Move station discharge pressures away from MOP 4 Reducedischarge pressure when throttle close to 100% 5 Reduce dischargepressure when throttle close to 0% 6 Reduce Discharge Pressures whenCooler Discharge Temperature close to Maximum 7 Reduce DischargePressure when when Engine needs to Start 8 Inform Dispatcher whenStation #5 Pd close to Customer #1pressure 9 Inform Dispatcher whenSupplier #3 Supply pressure close to Dominion pressure 10 InformDispatcher when Customer #2 Delivery pressure close to Dominion pressure

TABLE 2 Description: 1-D Array Variables: SO_(c) 0: Station is Bypassed1: Station has at least one unit Online on a loop controlled by MVCETHP_(c) Total available HP of units that are running now at stationNTHP_(c) New Total HP computed by Transition when a Compressor needs toStart All_HP_On_(c) 0: There is a future Transition that will requireadditional net HP to come online 1: No future HP increase is requiredON_(i) 0: Loop i is off 1: If MVC is running, then MVC is controllingLoop i UCC_(i) Unit Change Cushion in Hours for lowering DPT_(t) when acompressor needs to start. (UCC_(i) = LIMIT_(ij)/24) CST_(j) CompressorStart Time (Datetime format) as computed in Transition Logic DP_(t)Discharge Pressure Value at Controlled Station DPMT_(t) MinimumAllowable MVC Pressure Target (OPLO from Engineer Screen 1) DPT_(t) MVCPressure Limit set by Dispatcher on MVCOC (MVC Operating Console)DPTC_(t) Adjusted MVC Pressure Target sent to Loop Controller MOP_(t)Maximum Operating Pressure (get from existing ONGDISP_MVCVAL1-6) atControlled Station CT_(t) Cooler Outlet Temperature CTL_(t) CoolerOutlet Temperature Limit that overrides primary station controllersetpoints SP_(t) Suction Pressure Setpoint at manipulated stationDPSN_(t) Discharge Pressure Setpoint at upstream end of control loopwhen all down stream stations are in Normal Mode DPSE_(t) DischargePressure Setpoint at upstream end of control loop when the down streamstation needs to start an engine DPS_(t) Most recent Discharge PressureSetpoint that was sent to compressor station at upstream end of controlloop SD_(s) Speed Driver at controlled station (upstream)SPerror-limit_(s) Maximum error allowed between SP_(i) and NEWSP_(i)before EVENT is dedaired NEWSP_(s) Next desired suction setpoint ascalulated on Engineer|screen HP_A_(s) Availabe HP at the stationcomputed by summing the EMM computed HP for each unit that is nowavailable in SCADA HP_R_(s) Required HP for the station as computed byBlack Box first itteration after Dispatcher switches “Dispatcher RequestMVC” = “ON” R_(s) R_(s) = 1 if Station is running (Flow Setpoint fromSCADA > 0). Applies only to Relay Loop in multi loop Stations 2-D ArrayVariables: MULT_(ij) Multiplier for DPTC adjustment (j > 0) or Expectedaverage rate of change for Station #1 Discharge Pressure while FT isincreasing (PSI/Minute) for Flow Target Adjustment (j = 0) EXP_(ij)Exponent for DPTC adjustment or Flow Target Adjustment LIMIT_(ij) Limitused in Flow Target Adjustment & Compressor Start Adjustment THRSLD_(ij)Threshold P, Q or SD at which X_(ij) is computed n_(ij) Damping factor:n is one less than the number of time cycles X_(ni) is averaged over (0<= n < 100) X0_(ij) jth Adjustment factor for ith Control Loop at itme n= zero (now) X_(ni) Table of n historical adjustment factors used foreach of i control loops (n dimensioned from 0-99)

Once the various adjustment factors have been determined and stored inthe array or other data structure, the system proceeds to determinewhich of the adjustment factors should be applied. The general processof applying the adjustment factors is shown in the schematic flowdiagram of FIG. 14. In general, an adjusted discharge pressure target iscalculated by adding a negative correction equal to the largest (e.g.,most negative) of the adjustment factors to a discharge pressure limitwhich is the highest pressure desired in the pipeline. A time averagingtechnique is used to smooth the response. If the calculated adjustedpressure target is less than minimum allowable pressure, the calculatedadjusted pressure target is set equal to the minimum allowable pressure.The adjusted pressure target is output to the multivariable controller,and the process continues by once again calculating a set of adjustmentfactors for the next cycle.

The foregoing describes the process of calculating ten adjustmentfactors, and, for ease of description, describes the process in certainrespects as using one- and two-dimensional arrays or other datastructures. However, as those of skill in the art will realize, each ofthe ten adjustment factors would generally be calculated for a pluralityof points along the pipeline. For example, in the exemplary test systemdescribed above with six compressor stations, the ten adjustment factorsmay be calculated for each and every one of the compressor stations.Therefore, the array or data structure used to contain the data may bemultidimensional. As shown by the index subscripts on the variablesshown in the figures and described in TABLE 1, certain of the variablesare expected to be multi-dimensional, although in a sufficiently simplesystem (e.g., one compressor station), they need not necessarily bemultidimensional. Embodiments of the invention may also store pastadjustment factors to use as predictive data for future calculations,for calibration, or for other purposes.

As was noted briefly above, although there are ten adjustment factors inthis example, embodiments of the invention may calculate and take intoaccount any number of adjustment factors.

Software, systems and methods according to embodiments of the inventionare also configured to handle linepack transitions, which are changes inthe amount of gas or liquid flowing in the pipeline. These linepacktransitions are typically caused by changes in demand, although theyneed not be. (Those of skill in the art will note that some of the upsetconditions described above, in effect, deal with linepack transition.)The linepack transitions may be predicted by the system by accessingpast flow data and examining patterns in demand over time, or the systemmay be given an explicit warning by a human operator and the proceduresinitiated manually.

In general, the pseudocode or set of tasks involved in linepacktransitions is as follows:

a. If pack increase:

-   -   Compare maximum power of compressors running now to maximum        power of compressors proposed by the energy minimization module        output (EMMO).    -   Using lowest horsepower configuration, compute minimum thru put        and fuel usage when pumped from average of beginning and ending        suction pressure to average of beginning and ending discharge        pressure.    -   Determine time and quantity of fuel used to increase pack to        correct level.    -   Run compressor station at this fuel rate until determined time.

b. If pack decrease:

-   -   Compare maximum power of compressors running now to maximum        power of compressors proposed by EMMO.    -   Using highest horsepower configuration, compute maximum thru put        and fuel usage when pumped from average of beginning and ending        suction pressure to average of beginning and ending discharge        pressure.    -   Determine time and quantity of fuel used to decrease pack to        correct level.    -   Run compressor station at this fuel rate until determined time.

c. After Pack is set to correct level at each running compressor stationas computed above, run compressor at flow rate and fuel usage level asdetermined by steady state EMMO solution.

-   -   Create EMMO Now and EMMO Future data bases    -   Use only compressor configuration of EMMO Now and Future        databases in calculating time required.

More specifically, FIG. 18 and FIG. 19 show steps for saving a revisedcurrent nomination and a future nomination, respectively. FIG. 20, FIG.21, FIG. 22, FIG. 23, FIG. 24, and FIG. 25 are flow charts illustratinga transition process according to an embodiment of the invention.

Embodiments of the invention may also provide a number of administrativetools, as shown in FIG. 3. The administrative tools are useful inmonitoring the performance of the system and in calibration of themodels. An outline of the functions of the administrative tools isdescribed below.

Tools used to monitor the performance of the system include (i) FuelAttainment and (ii) Dispatcher Reporting. Fuel Attainment is an off-linetool that uses combinations of planned nomination data, systemcapabilities, historical measured data and pipeline models to determinethe calculated fuel usage for the pipeline for given time periods.Results of the Fuel Attainment combinations can be compared to actualmeasured fuels and to each other to determine system efficiencies androot causes for inefficiencies. Fuel Attainment combinations include thefollowing named results:

(i) Plan Optimum—Optimized amount of fuel that would have beencalculated if full capabilities were available. (all Stations andCompressors are available for selection)

(ii) Plan—Optimized amount of calculated fuel given planned nominationdata and available system capabilities at the time the optimizationoccurred.

(iii) Operational Optimum—Optimized amount of calculated fuel holdingcompressor configurations to their planned nomination settings, stationtargets to their respective maximum system capabilities, and usingactual data for all other model inputs.

(iv) Flow Variance—Same as Operational Optimum while holding all flowsexternal to the pipeline model constant at their planned rate.

(v) Temperature Variance—Same as Operational Optimum while holding alltemperatures used in the model to their planned values.

(vi) Pressure Variance—Same as Operational Optimum while holding allinlet and outlet pressures used in the model to the planned values.

(vii) Pressure Target Variance—Same as Operational Optimum while holdingall station targets to the planned values.

(viii) Units Used Variance—Same as Operational Optimum while holding allcompressor usage to the planned values.

Dispatcher Reporting is an on-line tool that uses real-time andhistorical data, system capabilities along with pipeline models todetermine the current calculated fuel efficiencies for the pipeline.This tool continually publishes current and summary shift efficienciesfor dispatchers for display on SCADA terminals.

FIG. 33, FIG. 34, FIG. 35, and FIG. 36 are flow charts illustratingsteps of a method for accurately measuring the fuel savings of thesystem according to an embodiment of the invention. Because of thevarious deliveries, supplies, and operating conditions, historicperformance indicators such as fuel per volume pumped may beinappropriate to accurately measure fuel savings. Rather, a moreaccurate measurement may be obtained by dividing the optimal(calculated) fuel+transition fuel by the actual fuel used.

Additionally, accurately measuring fuel savings includes taking accountof operational variances, such as when inputs into a forecastedoptimization run vary from the actual inputs. For example, it isimportant to capture the fuel costs when a compressor goes down or isnot available to run in the optimal solution. Costs may then bequantified to help determine whether to proceed with additionalmaintenance programs or capital expenditures. FIG. 37 shows fuelattainment variances that may be taken into account. Table 3 belowprovides definitions for the variances shown in FIG. 37.

TABLE 3 Variance Definitions 1. Planned Units Available Variance % =|[i] Plan Optimum − [ii] Plan|/[ii] Plan 2. Plan Attainment =[iii]Operational Optimum/[ii] Plan 3. Fuel Attainment = [iii]Operational Optimum/[0]Actual Fuel Used 4. Flow Variance % = |[iv] FlowVariance − [iii] Operational Optimum|/[iii] Operational Optimum 5.Temperature Variance % = |[v] Temperature Variance − [iii] OperationalOptimum|/[iii] Operational Optimum 6. Pressure Variance % = |[vi]Pressure Variance − [iii] Operational Optimum|/[iii] Operational Optimum7. Pressure Target Variance % = |[vii] Pressure Target Variance −[iii]Operational Optimum|/[iii] Operational Optimum 8. Units UsedVariance % = |[viii] Units Used Variance − [iii] OperationalOptimum|/[iii] Operational Optimum

The tool used for calibrating the models is the Automated CalibrationTool, which is designed to give the engineers appropriate data toanalyze and calibrate the models. To ensure reliable calibration, thesystem finds an appropriate steady state interval for calibration byensuring that the pipeline data is sufficiently static and unchangingover a programmed number of consecutive hours. For example, the initialprogram may download actual data after automatically finding 6consecutive hours of static operating conditions. Calculations relatingto the models' data are then compared to actual data. These values arestored and the engineer is notified when the values fall outside ofdesigned limits.

Included in the Automated Calibration Tool is:

(i) Segment Calibration—Compares model results to actual results forline segments. The parameters used to calibrate the model can include,but is not limited to:

Efficiency

Roughness

Heat Coefficient

Ground Temperature

Gas Temperature

(ii) Station Calibration—Compares model results to actual results forstations. Stations are comprised of differing compressor unit types andnumbers. The calibration procedure will be a combination of station andunit level configurations. The parameters used to calibrate the modelcan include, but is not limited to:

Inlet Pressure drop

Outlet Pressure drop

Temperature drop through coolers

Reciprocating Units:

Swept volume

Compression Ratio

Maximum and minimum clearance

Maximum and minimum horsepower

Heat rise coefficient

Fuel

Volume

Mechanical efficiency factor

Compressor efficiency factor

Centrifugal Units:

Maximum and minimum horsepower

Head curve

Mechanical efficiency factor

Heat rise coefficient

Fuel

Volume

(iii) Model Variance—Non-optimized amount of calculated fuel givenactual system utilization, measured data, and pipeline models. Pipelinemodels for Model Variance are connected, unlike Station and SegmentCalibration, which considers station and pipeline segments in isolation.The formula for Model Variance is:Mvar(Nom)=Model(Nom,SystemUtil(Nom),Mdata(Nom)−FuelActual(Nom)Where:Nom—Time-period between transient conditions.SystemUtil(Nom)—Actual System Utilization during the nomination. (i.e.compressor statuses, station utilization, etc.Mdata(Nom)—Average Measured data during the nomination.Model(Nom,SystemUtil(Nom),Mdata(Nom))—A function that calculatesunoptimized fuel usage for a given pipeline, nomination, systemutilization, and measured data.Mvar(Nom)—Calculated Model Variance during a nomination.

FIG. 26 shows general calibration processes that may be completed eachday as a scheduled event or run on demand. FIG. 27, FIG. 28, FIG. 29,FIG. 30, FIG. 31, and FIG. 32 are schematic flow diagrams illustratingtasks involved in calibrating the system. Model, segment and stationcalibration processes store streams of calculated data that may becompared to actual measured data, thereby allowing a determination to bemade whether a specific model is properly calibrated. When a section ofcalculated and actual data streams are within a defined tolerance, themodel is within calibration limits for that particular time-period.

Model validation is the process of automatically comparing streams ofactual measured data to streams of calculated data. This process usespredefined validation parameters for each stream pair to: (i) validatecalculated data; (ii) create alarms for calculated data that is out oftolerance; and (iii) create a calculated-data archive that may begraphed later by the user.

Validation may be configured to allow any actual measured data stream tobe compared to any calculated data stream using either an absolute orpercentage tolerance.

Validation using absolute tolerance is successful when:

$0 < {\left( {\sum\limits_{n = {First}}^{n = {Last}}{{{{Stream}_{A}\lbrack n\rbrack}_{Calculated} - {{Stream}_{B}\lbrack n\rbrack}_{Actual}}}} \right)/\left( {{Last} - {First}} \right)} < {{Absolute}\mspace{14mu}{Tolerance}_{j}}$Where Last is the most recent hour of the data stream to be validatedand First is the oldest hour of the data stream to be validated.

Validation using percentage tolerance is successful when PercentageTolerance Successful when:

$0 < {\left( {\sum\limits_{n = {First}}^{n = {Last}}{{{{Stream}_{A}\lbrack n\rbrack}_{Actual}/{{Stream}_{B}\lbrack n\rbrack}_{Calculated}}}} \right)/\left( {{Last} - {First}} \right)} < {\%\mspace{14mu}{Tolerance}_{j}}$Where Last is the most recent hour of the data stream to be validatedand First is the oldest hour of the data stream to be validated.

Tasks involved in the calibration include those shown in FIG. 27, FIG.28, FIG. 29, FIG. 30, FIG. 31, and FIG. 32 and described above in thepseudocode. Data from each segment of the pipeline is examined,particularly those factors identified above in the pseudocode, and themodels are corrected using the actual measured data from the pipeline.

The Automated Calibration Tool is not limited to fluid pipelinecalibration. Rather, the calibration process may effectively usedwherever models (steady-state or transient) are employed. Further, theAutomated Calibration Tool may significantly reduce the effort requiredto calibrate a system. For example, the Automated Calibration Toolutilized nearly 300 model inputs. As a rough estimate, it may takeapproximately 1 hour per input to collect data and perform the necessarycalculations. Hence, an engineer would take roughly 300 hours to tunethe models once. However, the Automated Calibration Tool performs thesecalculations on a daily basis. This significantly reduces man hoursrequired to calibrate models and helps ensure that the models arerunning safely and efficiently.

As noted above, pipeline flow optimization systems, software, andmethods may emulate transient optimization by: (i) maintaining theoptimal steady-state configurations and properly adjusting the optimalsteady-state pressure targets through upset conditions employing “BlackBox” logic; (ii) identifying through “Black Box” logic, existing fieldcompressor logic, or supply or delivery change of the predictive models,when a new compressor configuration may be required; (iii) transitioningto the new optimal solution as quickly as possible; (iv) ensuring thatthe results are accurate through the verification process of theAutomated Calibration Tool; and (v) quantifying the results of thepipeline optimization process through the Fuel Attainment process.Embodiments of the invention may provide for significant fuel reductionsin compressor stations, thereby resulting in significant fuel savingswhile operating a pipeline.

Although the invention has been described with respect to certainembodiments, the embodiments described herein are intended to beexemplary, rather than limiting. Modifications and variations will occurto those of ordinary skill in the art, and may be made without departingfrom the scope of the invention, which is reflected in the followingclaims.

1. A pipeline flow control optimization system comprising: a pipelineoptimization module comprising at least one optimization model, whereinthe pipeline optimization module is configured to produce a simulatedoptimum compressor configuration for a plurality of compressors on apipeline; a multivariable controller configured to control the pluralityof compressors on the pipeline based on the simulated optimum compressorconfiguration, the multivariable controller further configured toprovide each controlled compressor with an optimal target setpoint; amonitor configured to monitor the pipeline, including the plurality ofcompressors on the pipeline, and to sense at least one upset condition;and an upset condition handler configured to react to at least one unsetcondition that was not simulated by the pipeline optimization module bycalculating an adjustment factor for at least one of the plurality ofcompressors, wherein the adjustment factor is calculated to prevent theat least one compressor from exceeding an operating parameter,calculating an optimal adjusted setpoint by applying the adjustmentfactor to the optimal target setpoint for the at least one of theplurality of compressors, and providing the optimal adjusted setpoint tothe multivariable controller, wherein the adjustment factor and optimaladjusted setpoint correct for the at least one upset condition that wasnot simulated by the pipeline optimization module.
 2. The pipeline flowcontrol optimization system of claim 1, further comprising: a linepacktransition handler adapted to calculate and implement transitions inlinepack in a pipeline system; and an administrative tool configured tomonitor a performance of the pipeline system and to provide data tocalibrate a model for steady-state and transient modeling of thepipeline system.
 3. The pipeline flow control optimization system ofclaim 2, wherein the transitions in linepack are from a present optimalstate to a future optimal state.
 4. The pipeline flow controloptimization system of claim 1, wherein the at least one upset conditioncomprises at least one condition selected from a group comprising: anintake flow being less than a target intake flow, a discharge pressurebeing greater than a target discharge pressure, a discharge pressureapproaching a maximum operating pressure, a speed driver of a compressorapproaching 100% of maximum available power, a speed driver of acompressor approaching 0% of minimum available power, a coolertemperature approaching a maximum temperature, and a flow conditionbeing greater or less than a contractual obligation at a particulardischarge point.
 5. The pipeline flow control optimization system ofclaim 2, wherein the administrative tool is configured to provide thedata after sensing consecutive hours of static operating conditionswithin the pipeline system.
 6. The pipeline flow control optimizationsystem of claim 5, wherein the administrative tool is configured toprovide the data after sensing six consecutive hours of static operatingconditions.
 7. The pipeline flow control optimization system of claim 2,wherein the administrative tool is further configured to monitor aperformance of an individual line segment and an individual station andto provide data to calibrate a model for steady-state and transientmodeling of the individual line segment and individual station.
 8. Thepipeline flow control optimization system of claim 7, wherein theadministrative tool is further configured to monitor a performance of acoupled line segment and station and to provide data to calibrate amodel for steady-state and transient modeling of the coupled linesegment and station.
 9. The pipeline flow control optimization system ofclaim 1, wherein the plurality of compressors comprises activecompressors and inactive compressors; and the upset condition handler isfurther configured to operate the active compressors near maximumavailable power before activating additional compressors.
 10. Thepipeline flow control optimization system of claim 1, wherein thesetpoint is at least one of a pressure setpoint, a horsepower setpoint,a speed driver setpoint, or a temperature setpoint.
 11. A pipeline flowcontrol optimization system comprising: an optimizer configured tocalculate a simulated optimum compressor configuration comprising aplurality of steady-state parameters for a plurality of components of apipeline system and to provide the simulated optimum compressorconfiguration to an upset condition handler; a multivariable controllerconfigured to control the plurality of components of the pipeline systemand to provide at least one pipeline system component with an optimaltarget setpoint; and a supervisory control and data acquisition systemconfigured to acquire pipeline system data, wherein the upset conditionhandler is configured to receive the pipeline system data from thesupervisory control and data acquisition system; react to at least oneupset condition that was not simulated by the optimizer by calculatingan optimal adjusted setpoint for the at least one pipeline systemcomponent, wherein the optimal adjusted setpoint is calculated to bewithin the operating parameters of the at least one pipeline systemcomponent; and provide the multivariable controller with the optimaladjusted setpoint, wherein the optimal adjusted setpoint corrects forthe at least one upset condition that was not simulated by theoptimizer.
 12. The pipeline flow control optimization system of claim11, wherein the upset condition handler is included within themultivariable controller.
 13. The pipeline flow control optimizationsystem of claim 11, wherein the at least one upset condition comprisesat least one condition selected from a group comprising: an intake flowbeing less than a target intake flow, a discharge pressure being greaterthan a target discharge pressure, a discharge pressure approaching amaximum operating pressure, a speed driver of a compressor approaching100% of maximum available power, a speed driver of a compressorapproaching 0% of minimum available power, a cooler temperatureapproaching a maximum temperature, and a flow condition being greater orless than a contractual obligation at a particular discharge point. 14.The pipeline flow control optimization system of claim 11, wherein theupset condition handler is further configured to provide a warning to anoperator in response to at least one upset condition.
 15. The pipelineflow control optimization system of claim 11, further comprising anadministrative tool configured to monitor a performance of the pipelinesystem and to provide data to calibrate a model for steady-state andtransient modeling of the pipeline system.
 16. The pipeline flow controloptimization system of claim 11, further comprising a linepacktransition handler configured to calculate and implement transitions inlinepack in the pipeline system.
 17. The pipeline flow controloptimization system of claim 11, wherein the plurality of compressorscomprises active compressors and inactive compressors; and the upsetcondition handler is further configured to operate the activecompressors near maximum available power before activating additionalcompressors.
 18. The pipeline flow control optimization system of claim11, wherein the setpoint is at least one of a pressure setpoint, ahorsepower setpoint, a speed driver setpoint, or a temperature setpoint.19. A pipeline flow control optimization system comprising: an optimizerconfigured to calculate a simulated optimum compressor configurationcomprising a plurality of steady-state parameters for a pipeline system;a control unit configured to control a plurality of pipeline systemcomponents that cause pipeline flow and to provide at least one pipelinesystem component with an optimal target setpoint; a data acquirerconfigured to acquire pipeline system component data and pipeline flowdata; and a control logic unit configured to analyze the pipeline systemcomponent data and the pipeline flow data provided by the data acquirer,detect at least one upset condition that was not simulated by theoptimizer, calculate an adjustment factor for at least one pipelinesystem component, wherein the adjustment factor is calculated to preventthe at least one pipeline system component from exceeding an operatingparameter, and adjust, without any input from a human operator, theoptimal target setpoint of the at least one pipeline system component byapplying the adjustment factor to the control unit, wherein theadjustment factor corrects for the at least one upset condition that wasnot simulated by the optimizer.
 20. The pipeline flow controloptimization system of claim 19, wherein the control logic unitcomprises: an upset condition handler configured to analyze the pipelinesystem component data and the pipeline flow data; and a multivariablecontroller configured to control the plurality of pipeline systemcomponents that cause the pipeline flow.
 21. The pipeline flow controloptimization system of claim 20, wherein the control logic unit furthercomprises a linepack transition handler configured to calculate andimplement transitions in linepack in the pipeline system.
 22. Thepipeline flow control optimization system of claim 19, furthercomprising an administrative tool configured to monitor a performance ofthe pipeline system and to provide data to calibrate a model forsteady-state and transient modeling of the pipeline system.
 23. Thepipeline flow control optimization system of claim 19, wherein the atleast one upset condition comprises at least one condition selected froma group comprising: an intake flow being less than a target intake flow,a discharge pressure being greater than a target discharge pressure, adischarge pressure approaching a maximum operating pressure, a speeddriver of a compressor approaching 100% of maximum available power, aspeed driver of a compressor approaching 0% of minimum available power,a cooler temperature approaching a maximum temperature, and a flowcondition being greater or less than a contractual obligation at aparticular discharge point.
 24. The pipeline flow control optimizationsystem of claim 19, wherein the control unit and the data acquirer arean integrated unit.
 25. The pipeline flow control optimization system ofclaim 19, wherein the plurality of pipeline system components comprisesactive compressors and inactive compressors; and the control logic unitis further configured to operate the active compressors near maximumavailable power before activating additional compressors.
 26. Thepipeline flow control optimization system of claim 19, wherein thesetpoint is at least one of a pressure setpoint, a horsepower setpoint,a speed driver setpoint, or a temperature setpoint.
 27. A method ofhandling transient conditions in a pipeline flow control optimizationsystem, the method comprising: calculating a simulated optimumcompressor configuration for a plurality of compressors on a pipeline;controlling the plurality of compressors on the pipeline based on thesimulated optimum compressor configuration; providing each controlledcompressor with an optimal target setpoint; calculating an adjustmentfactor for at least one of the plurality of controlled compressors,wherein the adjustment factor is calculated to prevent the at least onecompressor from exceeding an operating parameter; choosing at least oneadjustment factor to apply to the pipeline flow control optimizationsystem; and applying the chosen at least one adjustment factor to thepipeline flow control optimization system by adjusting the optimaltarget setpoint for the at least one of the plurality of controlledcompressors, wherein the chosen at least one adjustment factor correctsfor at least one unset condition that was not simulated in the simulatedoptimum compressor configuration.
 28. The method of claim 27, whereinapplying the chosen at least one adjustment factor comprisesautomatically adjusting control parameters of the pipeline flowoptimization system.
 29. The method of claim 27, wherein applying thechosen at least one adjustment factor comprises notifying a humanoperator.
 30. The method of claim 27, further comprising: determiningwhether to adjust a target prior to calculating the adjustment factor;and initializing a data array.
 31. The method of claim 30, furthercomprising storing the adjustment factor, wherein storing one or moreadjustment factors comprises storing adjustment factors in the dataarray.
 32. The method of claim 27, further comprising: operating anactive compressor near maximum available power before activating anadditional compressor.
 33. The method of claim 27, wherein the setpointis at least one of a pressure setpoint, a horsepower setpoint, a speeddriver setpoint, or a temperature setpoint.